An adaptive radial basis function neural network (RBFNN) control of energy storage system for output tracking of a permanent magnet wind generator

The converters of a permanent magnet synchronous generator have to be properly controlled to achieve maximum transfer of energy from wind. To achiev e this goal, this article employs an energy storage device consisting of an energy capacitor interfaced through a voltage source converter which is operated through a smart adaptive radial basis function neural network (RBFNN) controller. The proposed adaptive strategy employs online neural network training as opposed to conventional procedure requiring offline training of a large data-set. The RBFNN controller was tested for various contingencies in the wind generator system. Th e adaptive online controller is observed to provide excellent damping profile following low grid voltage conditions as well as for other large disturbances. The controlled converter DC capacitor voltage helps maintain a smooth flow of real and reactive power in the system.